Secure and robust machine learning for healthcare: A survey
Recent years have witnessed widespread adoption of machine learning (ML)/deep learning
(DL) techniques due to their superior performance for a variety of healthcare applications …
(DL) techniques due to their superior performance for a variety of healthcare applications …
Neural natural language processing for unstructured data in electronic health records: a review
Electronic health records (EHRs), digital collections of patient healthcare events and
observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and …
observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and …
[HTML][HTML] A large language model for electronic health records
There is an increasing interest in developing artificial intelligence (AI) systems to process
and interpret electronic health records (EHRs). Natural language processing (NLP) powered …
and interpret electronic health records (EHRs). Natural language processing (NLP) powered …
[HTML][HTML] AI in health: state of the art, challenges, and future directions
F Wang, A Preininger - Yearbook of medical informatics, 2019 - thieme-connect.com
Introduction: Artificial intelligence (AI) technologies continue to attract interest from a broad
range of disciplines in recent years, including health. The increase in computer hardware …
range of disciplines in recent years, including health. The increase in computer hardware …
Clinical concept extraction using transformers
Objective The goal of this study is to explore transformer-based models (eg, Bidirectional
Encoder Representations from Transformers [BERT]) for clinical concept extraction and …
Encoder Representations from Transformers [BERT]) for clinical concept extraction and …
Multi-domain clinical natural language processing with MedCAT: the medical concept annotation toolkit
Electronic health records (EHR) contain large volumes of unstructured text, requiring the
application of information extraction (IE) technologies to enable clinical analysis. We present …
application of information extraction (IE) technologies to enable clinical analysis. We present …
Biomedical and clinical English model packages for the Stanza Python NLP library
Objective The study sought to develop and evaluate neural natural language processing
(NLP) packages for the syntactic analysis and named entity recognition of biomedical and …
(NLP) packages for the syntactic analysis and named entity recognition of biomedical and …
[HTML][HTML] A survey on recent named entity recognition and relationship extraction techniques on clinical texts
Significant growth in Electronic Health Records (EHR) over the last decade has provided an
abundance of clinical text that is mostly unstructured and untapped. This huge amount of …
abundance of clinical text that is mostly unstructured and untapped. This huge amount of …
Gatortron: A large clinical language model to unlock patient information from unstructured electronic health records
There is an increasing interest in developing artificial intelligence (AI) systems to process
and interpret electronic health records (EHRs). Natural language processing (NLP) powered …
and interpret electronic health records (EHRs). Natural language processing (NLP) powered …
[HTML][HTML] Bert-based ranking for biomedical entity normalization
Developing high-performance entity normalization algorithms that can alleviate the term
variation problem is of great interest to the biomedical community. Although deep learning …
variation problem is of great interest to the biomedical community. Although deep learning …